文章基本信息

标题：MONTE CARLO RESULTS ON THE SEMIPARAMETRIC NEAREST NEIGHBOR ESTIMATOR
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作者：Ken Inoue
期刊名称：JOURNAL OF THE JAPAN STATISTICAL SOCIETY
印刷版ISSN：1882-2754
电子版ISSN：1348-6365
出版年度：1999
卷号：29
期号：2
页码：163-179
DOI：10.14490/jjss1995.29.163
出版社：JAPAN STATISTICAL SOCIETY
摘要：Robinson (1987) proposed to use a nearest neighbor approach to estimate the regression coefficient in a heteroskedastic linear model. While this estimator is asymptotically efficient, it has been said to be inefficient in small samples compared with other semiparametric estimators such as those using a kernel. Like other semiparametric methods, his estimators of variances, which are used to get the weighted least squares estimator of the regression coefficient, are constructed as a weighted sum of the squared ols residuals. As Robinson (1987) indicated himself, however, there exists a sample splitting problem in his estimator and this may cause the small-sample inefficiency. Therefore a slight modification improves the small sample property of the k-NN estimator. In this paper we report Monte Carlo experiments and show that the modified Nearest Neighbor estimator has a sufficient level of efficiency in small samples.
关键词：semiparametric regression;heteroskedasticity;nearest neighbor method;sample splitting